In this paper we also examine how navigation problems can be simplified so that Deep RL can be utilized effectively. We propose a technique that uses a high-level action space. These actions are sub-trajectories of variable duration and shape. High-level actions have been examined in a range of contexts for planning. For example, the maneuver automaton consists of trims and maneuvers that can be combined to create end-to-end kinodynamic plans [1]}, [2]}, [3]}, [4]}. Similarly, motions can be created using dynamic movement primitives (DMPs) [5]}, [6]}. These primitives are represented using dynamical systems theory and can be modulated and combined to achieve complex behaviors. Each DMP is characterized by an intermediate goal parameter and shape parameters which decide how the intermediate goal is reached. DMPs have been applied to robotic flight in [7]} where individual primitives are learned directly from training trajectory demonstrations.
